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26 April 2016 Fast whole-brain optical tomography capable of automated slice-collection (Conference Presentation)
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Acquiring brain-wide composite information of neuroanatomical and molecular phenotyping is crucial to understand brain functions. However, current whole-brain imaging methods based on mechnical sectioning haven’t achieved brain-wide acquisition of both neuroanatomical and molecular phenotyping due to the lack of appropriate whole-brain immunostaining of embedded samples. Here, we present a novel strategy of acquiring brain-wide structural and molecular maps in the same brain, combining whole-brain imaging and subsequent immunostaining of automated-collected slices. We developed a whole-brain imaging system capable of automatically imaging and then collecting imaged tissue slices in order. The system contains three parts: structured illumination microscopy for high-throughput optical sectioning, vibratome for high-precision sectioning and slice-collection device for automated collecting of tissue slices. Through our system, we could acquire a whole-brain dataset of agarose-embedded mouse brain at lateral resolution of 0.33 µm with z-interval sampling of 100 µm in 9 h, and automatically collect the imaged slices in sequence. Subsequently, we performed immunohistochemistry of the collected slices in the routine way. We acquired mouse whole-brain imaging datasets of multiple specific types of neurons, proteins and gene expression profiles. We believe our method could accelerate systematic analysis of brain anatomical structure with specific proteins or genes expression information and understanding how the brain processes information and generates behavior.
Conference Presentation
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Jing Yuan, Tao Jiang, Lei Deng, Beng Long, Jie Peng, Qingming Luo, and Hui Gong "Fast whole-brain optical tomography capable of automated slice-collection (Conference Presentation)", Proc. SPIE 9690, Clinical and Translational Neurophotonics; Neural Imaging and Sensing; and Optogenetics and Optical Manipulation, 969013 (26 April 2016);

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